Deep vs. Shallow Learning-based Filters of MSMS Spectra in Support of Protein Search Engines.

ABSTRACT: Despite the linear relation between the number of observed spectra and the searching time, the current protein search engines, even the parallel versions, could take several hours to search a large amount of MSMS spectra, which can be generated in a short time. After a laborious searching process, some (and at times, majority) of the observed spectra are labeled as non-identifiable. We evaluate the role of machine learning in building an efficient MSMS filter to remove non-identifiable spectra. We compare and evaluate the deep learning algorithm using 9 shallow learning algorithms with different configurations. Using 10 different datasets generated from two different search engines, different instruments, different sizes and from different species, we experimentally show that deep learning

SUBMITTER: Maabreh M 

PROVIDER: S-EPMC8370709 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature